---
title: "Global Plastic Waste"
output:
  flexdashboard::flex_dashboard:
    orientation: columns
    social: menu
    source_code: embed
---

```{r setup, include=FALSE}
#https://github.com/LaineyJ/TidyTuesdays/blob/master/2019-05-21/Global%20Plastic%20Waste.R
#install.packages("flexdashboard")
#install.packages("janitor")
#install.packages("extrafont")
#install.packages("gridExtra")
#install.packages("dplyr")
#install.packages("tidyverse")
#install.packages("ggplot2")
#install.packages("gifski")
#install.packages("png")
#install.packages("transformr")
#install.packages("plotly")
#install.packages("flexdashboard")
#install.packages("RColorBrewer")
#install.packages("ggforce")
#install.packages("gganimate")
library(janitor)
library(extrafont)
library(gridExtra)
library(tidyverse)
library(gifski)
library(png)
library(transformr)
library(ggplot2)
library(gganimate)
library(flexdashboard)
library(ggforce)
library(RColorBrewer)
library(gridExtra)
library(plotly)
library(dplyr)
```

Column
-----------------------------------------------------------------------

### Plastic waste produced in 2010
```{r}
wasteinyears<-readr::read_csv("./Data/global-plastics-production.csv")
plasticwaste_2010<-readr::read_csv("./Data/plastic-waste-generation-total.csv")
plasticfate<-readr::read_csv("./Data/global-plastic-fate.csv")
#plasticfate
#glimpse(plasticfate)

plasticwaste_2015<-readr::read_csv("./Data/plastic-waste-by-sector.csv")
#plasticwaste_2015

plasticpercapita<-readr::read_csv("./Data/plastic-waste-per-capita.csv")

library(plotly)
# light grey boundaries
l <- list(color = toRGB("grey"), width = 0.5)
# specify map projection/options
g <- list(
  showframe = FALSE,
  showcoastlines = FALSE,
  projection = list(type = 'Mercator')
)
plot_geo(plasticwaste_2010) %>%
  add_trace(
    z=~`Plastic waste generation (tonnes, total) (tonnes per year)`, color = ~`Plastic waste generation (tonnes, total) (tonnes per year)` , colors = 'Reds',
     locations = ~Code, marker = list(line = l)
  ) %>%
  colorbar(title = 'Plastic waste in Million Tonnes', ticksuffix = '') %>%
  layout(
    title = 'Plastic waste produced in 2010',
    geo = g
  )
```
Column
-----------------------------------------------------------------------

### Plastic waste generated per person per day in 2010

```{r boxplot}
#install.packages("plotly")
library(plotly)
# light grey boundaries
l <- list(color = toRGB("grey"), width = 0.5)
# specify map projection/options
g <- list(
  showframe = FALSE,
  showcoastlines = FALSE,
  projection = list(type = 'Mercator')
)
plot_geo(plasticpercapita) %>%
  add_trace(
    z=~`Per capita plastic waste (kilograms per person per day)`, color = ~`Per capita plastic waste (kilograms per person per day)` , colors = 'Blues',
     locations = ~Code, marker = list(line = l)
  ) %>%
  colorbar(title = 'Plastic waste in Million Tonnes', ticksuffix = 'kg') %>%
  layout(
    title = 'Plastic Waste Generated per person per day in 2010',
    geo = g
  )
```


Column
-----------------------------------------------------------------------

### Plastic waste generated per person per day in Kg's


```{r barplot}

plasticfate<-readr::read_csv("./Data/global-plastic-fate.csv")
#plasticfate
#glimpse(plasticfate)

plasticwaste_2015<-readr::read_csv("./Data/plastic-waste-by-sector.csv")
#plasticwaste_2015

plasticpercapita<-readr::read_csv("./Data/plastic-waste-per-capita.csv")
#plasticpercapita
#glimpse(plasticpercapita)
inadequateplastic<-readr::read_csv("./Data/inadequately-managed-plastic.csv")
#inadequateplastic
# Read in the data
coast_vs_waste <- readr::read_csv("https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2019/2019-05-21/coastal-population-vs-mismanaged-plastic.csv")
mismanaged_vs_gdp <- readr::read_csv("https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2019/2019-05-21/per-capita-mismanaged-plastic-waste-vs-gdp-per-capita.csv")
waste_vs_gdp <- readr::read_csv("https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2019/2019-05-21/per-capita-plastic-waste-vs-gdp-per-capita.csv")
continent <- readr::read_csv("https://datahub.io/JohnSnowLabs/country-and-continent-codes-list/r/country-and-continent-codes-list-csv.csv")
# Clean up names
coast_vs_waste <- clean_names(coast_vs_waste)
mismanaged_vs_gdp <- mismanaged_vs_gdp %>%
  set_names(c("entity", "code", "year", "mismg_kg_pp_pd", "gdp_per_capita", "total_population_gapminder"))
waste_vs_gdp <- waste_vs_gdp %>%
  set_names(c("entity", "code", "year", "waste_kg_pp_pd", "gdp_per_capita", "total_population_gapminder"))
continent <- clean_names(continent)
continent$continent_code <- ifelse(is.na(continent$continent_code), "NA", continent$continent_code)

# Merge dfs
waste_df <- merge(coast_vs_waste, mismanaged_vs_gdp, by = c("code", "year"))
waste_df <- merge(waste_df, waste_vs_gdp, by = c("code", "year"))
waste_df <- merge(waste_df, continent, by.x = "code", by.y = "three_letter_country_code")
waste_df <- select(waste_df, -c(country_name, two_letter_country_code, country_number))

# Exclude countries with no data
no_data <- waste_df %>%
  filter(is.na(mismanaged_plastic_waste_tonnes) & is.na(mismg_kg_pp_pd) & is.na(waste_kg_pp_pd) & year == 2010) %>%
  select(code, entity)

waste_df <- waste_df %>%
  filter(!code %in% no_data$code) %>%
  select(-c(entity.x, entity.y, total_population_gapminder.x, total_population_gapminder.y, gdp_per_capita.x)) %>%
  rename(gdp_per_capita = gdp_per_capita.y) %>%
  mutate(coastal_prop = coastal_population / total_population_gapminder)
waste_df$coastal_prop <- ifelse(waste_df$coastal_prop > 1, 1, waste_df$coastal_prop)

# Filter to only 2010 data
waste_2010 <- waste_df %>%
  filter(!is.na(waste_kg_pp_pd) & !is.na(mismanaged_plastic_waste_tonnes) & !is.na(mismg_kg_pp_pd) &
           !is.na(gdp_per_capita) & !is.na(total_population_gapminder & waste_kg_pp_pd < 1)) %>%
  select(-year)

plt_l <- waste_2010 %>%
  ggplot(aes(x = coastal_population, y = mismanaged_plastic_waste_tonnes)) +
  geom_point(alpha = 0.75, aes(size = total_population_gapminder, color = continent_name)) +
  geom_smooth(method = "lm", se = FALSE, color = "grey20", size = 0.75, linetype = "dotted") +
  geom_smooth(data = filter(waste_2010, continent_name == "Oceania"), method = "lm",
              se = FALSE, color = "grey20", size = 0.75, linetype = "dashed")+
  scale_y_log10()+scale_x_log10()
ggplotly(plt_l)



```

Column
-----------------------------------------------------------------------

### Gdp_per_capita vs the Mismanaged plastic waste per person per day

```{r}

plt_gdp <- waste_2010 %>%
  ggplot(aes(x = gdp_per_capita, y = mismg_kg_pp_pd)) +
  geom_point(alpha = 0.75, aes(size = total_population_gapminder, color = continent_name)) +
  # geom_smooth(method = "lm", se = FALSE, color = "grey20", size = 0.75, linetype = "dotted") +
  # geom_smooth(data = filter(waste_2010, continent_name == "Oceania"), method = "lm",
  #             se = FALSE, color = "grey20", size = 0.75, linetype = "dashed")+
  scale_x_log10()
ggplotly(plt_gdp)


```